Genome-wide meta-analysis of problematic alcohol use in 435,563 individuals yields insights into biology and relationships with other traits

Zhou, Hang; Sealock, Julia M.; Sanchez-Roige, Sandra; Clarke, Toni‐Kim; Levey, Daniel F.; Cheng, Zhongshan; Li, Boyang; Polimanti, Renato; Kember, Rachel L; Smith, Rachel Vickers; Thygesen, Johan H; Morgan, Marsha Y; Atkinson, Stephen R.; Thursz, Mark R; Nyegaard, Mette; Mattheisen, Manuel; Børglum, Anders D.; Johnson, Emma C.; Justice, Amy C; Palmer, Abraham A; McQuillin, Andrew; Davis, Lea K; Edenberg, Howard J; Agrawal, Arpana; Kranzler, Henry R; Gelernter, Joel

doi:10.1038/s41593-020-0643-5

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Genome-wide meta-analysis of problematic alcohol use in 435,563 individuals yields insights into biology and relationships with other traits

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Author(s): Hang Zhou ¹ ^, ² , Julia M. Sealock ³ ^, ⁴ , Sandra Sanchez-Roige ⁵ , Toni-Kim Clarke ⁶ , Daniel F. Levey ¹ ^, ² , Zhongshan Cheng ¹ ^, ² , Boyang Li ⁷ , Renato Polimanti ¹ ^, ² , Rachel L. Kember ⁸ ^, ⁹ , Rachel Vickers Smith ¹⁰ , Johan H. Thygesen ¹¹ , Marsha Y. Morgan ¹² , Stephen R. Atkinson ¹³ , Mark R. Thursz ¹³ , Mette Nyegaard ¹⁴ ^, ¹⁵ ^, ¹⁶ ^, ¹⁷ , Manuel Mattheisen ¹⁴ ^, ¹⁸ ^, ¹⁹ , Anders D. Børglum ¹⁴ ^, ¹⁵ ^, ¹⁶ ^, ¹⁷ , Emma C. Johnson ²⁰ ^, ²¹ , Amy C. Justice ² ^, ²¹ ^, ²² , Abraham A. Palmer ⁵ ^, ²³ , Andrew McQuillin ¹¹ , Lea K. Davis ³ ^, ⁴ ^, ²⁴ , Howard J. Edenberg ²⁵ ^, ²⁶ , Arpana Agrawal ²⁰ , Henry R. Kranzler ⁹ ^, ²⁷ ^, ^* , Joel Gelernter ¹ ^, ² ^, ²⁸ ^, ^*

Publication date (Electronic): 25 May 2020

Journal: Nature neuroscience

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Abstract

Problematic alcohol use (PAU) is a leading cause of death and disability worldwide. Although genome-wide association studies (GWASs) have identified PAU risk genes, the genetic architecture of this trait is not fully understood. We conducted a proxy-phenotype meta-analysis of PAU combining alcohol use disorder and problematic drinking in 435,563 European-ancestry individuals. We identified 29 independent risk variants, 19 of them novel. PAU was genetically correlated with 138 phenotypes, including substance use and psychiatric traits. Phenome-wide polygenic risk score analysis in an independent biobank sample (BioVU, n=67,589) confirmed the genetic correlations between PAU and substance use and psychiatric disorders. Genetic heritability of PAU was enriched in brain and in conserved and regulatory genomic regions. Mendelian randomization suggested causal effects on liability to PAU of substance use, psychiatric status, risk-taking behavior, and cognitive performance. In summary, this large PAU meta-analysis identified novel risk loci and revealed genetic relationships with numerous other traits.

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A global reference for human genetic variation

Lachlan Coin, Robert Garry, Oleksyk Taras (2018)

The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.

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Second-generation PLINK: rising to the challenge of larger and richer datasets

Christopher Chang, Carson Chow, Laurent Tellier … (2015)

PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for even faster and more scalable implementations of key functions. In addition, GWAS and population-genetic data now frequently contain probabilistic calls, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1's primary data format. To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, O(sqrt(n))-time/constant-space Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. This will be followed by PLINK 2.0, which will introduce (a) a new data format capable of efficiently representing probabilities, phase, and multiallelic variants, and (b) extensions of many functions to account for the new types of information. The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.

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Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

Jack Bowden, George Davey Smith, Stephen Burgess (2016)

Background: The number of Mendelian randomization analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. However, some genetic variants may not be valid instrumental variables, in particular due to them having more than one proximal phenotypic correlate (pleiotropy). Methods: We view Mendelian randomization with multiple instruments as a meta-analysis, and show that bias caused by pleiotropy can be regarded as analogous to small study bias. Causal estimates using each instrument can be displayed visually by a funnel plot to assess potential asymmetry. Egger regression, a tool to detect small study bias in meta-analysis, can be adapted to test for bias from pleiotropy, and the slope coefficient from Egger regression provides an estimate of the causal effect. Under the assumption that the association of each genetic variant with the exposure is independent of the pleiotropic effect of the variant (not via the exposure), Egger’s test gives a valid test of the null causal hypothesis and a consistent causal effect estimate even when all the genetic variants are invalid instrumental variables. Results: We illustrate the use of this approach by re-analysing two published Mendelian randomization studies of the causal effect of height on lung function, and the causal effect of blood pressure on coronary artery disease risk. The conservative nature of this approach is illustrated with these examples. Conclusions: An adaption of Egger regression (which we call MR-Egger) can detect some violations of the standard instrumental variable assumptions, and provide an effect estimate which is not subject to these violations. The approach provides a sensitivity analysis for the robustness of the findings from a Mendelian randomization investigation.

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Author and article information

Journal

Journal ID (nlm-journal-id): 9809671

Journal ID (pubmed-jr-id): 21092

Journal ID (nlm-ta): Nat Neurosci

Journal ID (iso-abbrev): Nat. Neurosci.

Title: Nature neuroscience

ISSN (Print): 1097-6256

ISSN (Electronic): 1546-1726

Publication date Nihms-submitted: 30 July 2020

Publication date (Electronic): 25 May 2020

Publication date (Print): July 2020

Publication date PMC-release: 25 November 2020

Volume: 23

Issue: 7

Pages: 809-818

Affiliations

[1 ]Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA

[2 ]Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA

[3 ]Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA

[4 ]Division of Medical Genetics, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

[5 ]Department of Psychiatry, University of California San Diego, La Jolla, CA, USA

[6 ]Division of Psychiatry, University of Edinburgh, Edinburgh, UK

[7 ]Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA

[8 ]Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

[9 ]Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA

[10 ]University of Louisville School of Nursing, Louisville, KY, USA

[11 ]Division of Psychiatry, University College London, London, UK

[12 ]UCL Institute for Liver & Digestive Health, Division of Medicine, Royal Free Campus, University College London, London, UK

[13 ]Department of Metabolism, Digestion & Reproduction, Imperial College London, London, UK

[14 ]Department of Biomedicine, Aarhus University, Aarhus, Denmark

[15 ]Centre for Integrative Sequencing, iSEQ, Aarhus University, Aarhus, Denmark

[16 ]The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Denmark

[17 ]Center for Genomics and Personalized Medicine, Aarhus, Denmark

[18 ]Department of Psychiatry, Psychosomatics and Psychotherapy, University of Würzburg, Würzburg, Germany

[19 ]Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden

[20 ]Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA

[21 ]Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA

[22 ]Center for Interdisciplinary Research on AIDS, Yale School of Public Health, New Haven, CT, USA

[23 ]Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA

[24 ]Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA

[25 ]Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA

[26 ]Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA

[27 ]Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

[28 ]Departments of Genetics and Neuroscience, Yale University School of Medicine, New Haven, CT, USA

Author notes

Author Contributions: H.Z., J.G., H.R.K., and A.A.P. conceived analyses; H.Z. and J.G. wrote the first draft and prepared all drafts for submission; JG supervised and HZ accomplished primary analyses; J.M.S., S.S.R., T.K.C., D.F.L., Z.C., B.L., and A.M. conducted additional analyses; J.G., H.R.K., A.A.P., L.K.D., H.J.E., and A.A. supervised additional analyses; J.M.S., S.S.R., T.K.C., A.A.P., A.M., and L.K.D. prepared individual datasets and provided summary statistics or results; R.P., R.L.K., R.V.S., J.H.T., M.Y.M., S.R.A., M.R.T., M.N., M.M., A.D.B., E.C.J., A.C.J., A.M., L.K.D., and H.R.K. provided critical support regarding phenotypes and data in individual datasets; J.G., A.C.J., and H.R.K. provided resource support. All authors reviewed the manuscript and approved it for submission.

Corresponding Author: Joel Gelernter, Department of Psychiatry, Yale School of Medicine, Veterans Affairs Connecticut Healthcare System, 116A2, 950 Campbell Ave, West Haven, CT 06516, USA. Phone: +1 (203) 494-6326 ×3590; Fax: +1 (203) 937-4741; joel.gelernter@ 123456yale.edu .

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Manuscript ID: NIHMS1585622

DOI: 10.1038/s41593-020-0643-5

PMC ID: 7485556

PubMed ID: 32451486

SO-VID: e43014b8-af59-4e18-8c09-4e77f019f4ea

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Genome-wide meta-analysis of problematic alcohol use in 435,563 individuals yields insights into biology and relationships with other traits

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UCL: UN SDG 03 Good Health and Well-Being

Most cited references 68

A global reference for human genetic variation

Second-generation PLINK: rising to the challenge of larger and richer datasets

Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

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